This category of data is the dominant form of expression on social media, mostly as a direct result of restriction on text length. Consequently, this type of text is rife with abbreviations to make efficient use of space on platforms such as Twitter. Two main approaches are utilized toward annotation of tweets: Manual and hashtag-based. [1]}, [2]}, [3]}, [4]} introduce manually annotated datasets of sarcastic utterances. Most annotation approaches in the literature are conducted using hashtags to create labeled datasets. Sarcastic intent in English is commonly and culturally communicated using hashtags such as #sarcasm, #sarcastic, #not. [5]}, [6]}, [7]} use hashtag-based datasets of tweets. [8]} only uses #not to collect and label their tweets. While collecting sarcastic tweets using this method is undemanding, the inclusion of non-sarcastic tweets can be challenging since tweets containing #notsarcastic may not represent a general non-sarcastic text [9]}. Another approach is to collect the non-sarcastic tweets of users whose sarcastic tweets are also present in the dataset. To ensure collection of true sarcasm, some studies like [10]} manually verified the initial hashtag-based tweets using annotators.
